// Ceres Solver - A fast non-linear least squares minimizer // Copyright 2010, 2011, 2012 Google Inc. All rights reserved. // http://code.google.com/p/ceres-solver/ // // Redistribution and use in source and binary forms, with or without // modification, are permitted provided that the following conditions are met: // // * Redistributions of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // * Redistributions in binary form must reproduce the above copyright notice, // this list of conditions and the following disclaimer in the documentation // and/or other materials provided with the distribution. // * Neither the name of Google Inc. nor the names of its contributors may be // used to endorse or promote products derived from this software without // specific prior written permission. // // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE // POSSIBILITY OF SUCH DAMAGE. // // Author: sameeragarwal@google.com (Sameer Agarwal) // // Cost term that implements a prior on a parameter block using a // normal distribution. #ifndef CERES_PUBLIC_NORMAL_PRIOR_H_ #define CERES_PUBLIC_NORMAL_PRIOR_H_ #include "ceres/cost_function.h" #include "ceres/internal/eigen.h" namespace ceres { // Implements a cost function of the form // // cost(x) = ||A(x - b)||^2 // // where, the matrix A and the vector b are fixed and x is the // variable. In case the user is interested in implementing a cost // function of the form // // cost(x) = (x - mu)^T S^{-1} (x - mu) // // where, mu is a vector and S is a covariance matrix, then, A = // S^{-1/2}, i.e the matrix A is the square root of the inverse of the // covariance, also known as the stiffness matrix. There are however // no restrictions on the shape of A. It is free to be rectangular, // which would be the case if the covariance matrix S is rank // deficient. class NormalPrior: public CostFunction { public: // Check that the number of rows in the vector b are the same as the // number of columns in the matrix A, crash otherwise. NormalPrior(const Matrix& A, const Vector& b); virtual bool Evaluate(double const* const* parameters, double* residuals, double** jacobians) const; private: Matrix A_; Vector b_; }; } // namespace ceres #endif // CERES_PUBLIC_NORMAL_PRIOR_H_